Abdulqader Mohammed Khalaf, Mohammed Abdel Razek, Mohamed El-dosuky, Ahmed Sobhi

Breast Cancer Detection and Classification Using Deep Learning Techniques Based On Ultrasound Image
Authors: Abdulqader Mohammed Khalaf, Mohammed Abdel Razek, Mohamed El-dosuky, Ahmed Sobhi (BEEI ID 8397)

Breast cancer ranks as the most prevalent form of cancer diagnosed in women, and diagnosis faces several challenges, a change in the size, shape and appearance of breasts, dense breast tissue, lumps or thickening in the breast especially if in only one breast, lumps and nodules in the breast. The major challenge that faces deep learning diagnosis of breast cancer was its shape, size and position non-uniformity especially malignant cancer. We and all researchers always strive through the computer‐aided diagnosis (CAD) system and others to provide assistance in the medical field to detect and classification the type of tumor. In this work proposed a deep learning system for analysis medical image that increased the accuracy of detection and classification of breast cancer types from ultrasound images. It reaches 99.29% accuracy, exceeding other previous work. First, image processing was applied to in enhance the quality of input images. Second, the image segmentation was performed using U-Net architecture. Third, many features are extracted using Mobilenet. Finally, classification using Visual Geometry Group VGG16, the accuracy of detection and classification using for proposed system was evaluated.

Supported by Master Program of Electrical and Computer Engineering, Universitas Ahmad Dahlan, https://mee.uad.ac.id #yogyakarta
Admission: https://mee.uad.ac.id/pendaftaran/

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